{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "sCXgqVWfnoiN"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from sympy import symbols, cos, sin\n",
    "from sympy.plotting import plot_parametric\n",
    "from math import sin,cos\n",
    "import io\n",
    "import cv2\n",
    "import pykalman\n",
    "from scipy import optimize\n",
    "import matplotlib as mpl\n",
    "from numpy import array, arange, abs as np_abs\n",
    "from numpy.fft import rfft, rfftfreq\n",
    "from math import sin, pi\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from scipy.io import wavfile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Лабораторная работа 3.1\n",
    "C помощью метода наименьших квадратов рассчитан КИХ фильтр c линейной ФЧХ пропускающий только частоты в\n",
    "диапазонах 50–150 Гц; 350–750 Гц; 900–1500 Г"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "GM6F1OraXOpP"
   },
   "outputs": [],
   "source": [
    "hz = 8000\n",
    "l = 0\n",
    "r = 1\n",
    "N = (r - l) * hz\n",
    "x = np.linspace(l, r, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "_owawKgOrdob"
   },
   "outputs": [],
   "source": [
    "d = hz / N\n",
    "fk = [d * pt for pt in range(int(-N / 2), int(N / 2))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 849
    },
    "executionInfo": {
     "elapsed": 2209,
     "status": "ok",
     "timestamp": 1607949421802,
     "user": {
      "displayName": "19parenchenkov98",
      "photoUrl": "",
      "userId": "07404199301160036105"
     },
     "user_tz": -180
    },
    "id": "XAf8TRTlJfh5",
    "outputId": "eb55b8a7-ef75-4d24-f04e-192ae551de05"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa605dd87f0>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = 10 # амплитуда компонент на пропускаемых частотах\n",
    "a1 = 5 # амплитуда компонент на непропускаемых частотах\n",
    "\n",
    "A1 = a\n",
    "A2 = a1\n",
    "A3 = a\n",
    "A4 = a1\n",
    "f1 = 100\n",
    "f2 = 2000\n",
    "f3 = 600\n",
    "f4 = 800\n",
    "\n",
    "x1 = [A1 * math.cos(2 * math.pi *  f1 * t) + A2 * math.cos(2 * math.pi * f2 * t) + A3 * math.cos(2 * math.pi * f3 * t) +\n",
    "       A4 * math.cos(2 * math.pi *  f4 * t) for t in x]\n",
    "ft = abs(np.fft.fftshift(np.fft.fft(x1)))\n",
    "\n",
    "plt.figure(figsize=(30, 20))\n",
    "plt.plot(fk, ft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LxbP0BlZaMtK"
   },
   "outputs": [],
   "source": [
    "min_fk = min(fk_8_khz)\n",
    "fk_normed = [(2 * math.pi * (f - min_fk)) / Fs_8_khz for f in fk_8_khz]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PQRT06bIsSXv"
   },
   "outputs": [],
   "source": [
    "min_f = min(fk_normed)\n",
    "max_f = max(fk_normed)\n",
    "len_f = max(fk_normed) - min(fk_normed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SNjW5nH-a5C8"
   },
   "outputs": [],
   "source": [
    "Ad = list([1 if (fk >= 50 and fk <= 150) or (fk >= 350 and fk <= 750) or (fk >= 900 and fk <= 1500) else 0 for fk in fk_8_khz[4000:]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "f0TuycPhbkAm"
   },
   "outputs": [],
   "source": [
    "for i in range(4000):\n",
    "    Ad.append(Ad[i])\n",
    "\n",
    "for i in range(4000):\n",
    "    Ad[i] = Ad[7999 - i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 851
    },
    "executionInfo": {
     "elapsed": 3909,
     "status": "ok",
     "timestamp": 1607949423525,
     "user": {
      "displayName": "19parenchenkov98",
      "photoUrl": "",
      "userId": "07404199301160036105"
     },
     "user_tz": -180
    },
    "id": "W3Lhr_lXpO0j",
    "outputId": "98b68fca-ce15-4705-b294-3fbfe23d0d45"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc5346e0ba8>]"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3240x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_s = np.arange(-4000, 4000, 100)\n",
    "\n",
    "# график для контроля правильности построения фильтра\n",
    "plt.figure(figsize=(45, 30))\n",
    "plt.title(\"Полосы пропускания требуемого фильтра\")\n",
    "plt.xticks(x_s)\n",
    "plt.plot(fk_8_khz, Ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7HsXEujBb8ui"
   },
   "outputs": [],
   "source": [
    "N = 111\n",
    "M = int((N - 1) / 2)\n",
    "L = 3 * M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6bYXQpnmcBYi"
   },
   "outputs": [],
   "source": [
    "w_s = np.linspace(0, 2 * math.pi, L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fwe52CBLTZZW"
   },
   "outputs": [],
   "source": [
    "step = int(8000 / L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FF4P_G20uZWj"
   },
   "outputs": [],
   "source": [
    "F = np.array([[2 * math.cos(w_s[j] * (M - i)) if i < M else 1 for i in range(M + 1)] for j in range(L)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6nV_3xJpmIOX"
   },
   "outputs": [],
   "source": [
    "ad = list([1 if (w >= 2 * math.pi * 50 / Fs_8_khz and w <= 2 * math.pi * 150 / Fs_8_khz)\n",
    "      or (w >= 2 * math.pi * 350 / Fs_8_khz and w <= 2 * math.pi * 750 / Fs_8_khz)\n",
    "      or (w >= 2 * math.pi * 900 / Fs_8_khz and w <= 2 * math.pi * 1500 / Fs_8_khz) else 0 for w in w_s])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Y8C8CAyP7WYs"
   },
   "outputs": [],
   "source": [
    "for i in range(int(L/2)):\n",
    "  ad[int(L/2) + i + 1] = ad[int(L/2) - i - 1]\n",
    "\n",
    "#for i in range(int(L/2)):\n",
    " # ad[int(L/2) - i] = ad[int(L/2) + i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "executionInfo": {
     "elapsed": 3895,
     "status": "ok",
     "timestamp": 1607949423539,
     "user": {
      "displayName": "19parenchenkov98",
      "photoUrl": "",
      "userId": "07404199301160036105"
     },
     "user_tz": -180
    },
    "id": "rMGWDN1-67Yx",
    "outputId": "ba48cc70-1d61-430c-96fd-7a2bfcd8fc2c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc53456e0f0>]"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(w_s, ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vgymK-yNveCY"
   },
   "outputs": [],
   "source": [
    "h_ = np.linalg.pinv(F) @ ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IlqqPy_oLNyC"
   },
   "outputs": [],
   "source": [
    "#h_full = [h_[i] if i < M else h_[M - i] for i in range(2 * M + 1)]\n",
    "h_full = [h_[i] if i <= M else h_[2 * M - i] for i in range(2 * M + 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AlfQA4sK4aCy"
   },
   "outputs": [],
   "source": [
    "res_sig = np.convolve(x_1, h_full, mode=\"same\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 847
    },
    "executionInfo": {
     "elapsed": 5855,
     "status": "ok",
     "timestamp": 1607949425519,
     "user": {
      "displayName": "19parenchenkov98",
      "photoUrl": "",
      "userId": "07404199301160036105"
     },
     "user_tz": -180
    },
    "id": "LnX72aVv6FjD",
    "outputId": "528370db-1063-4b4b-94d8-52b2c8d41f3b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc5344e9c18>"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3240x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discr_fourier_pts_res = abs(np.fft.fftshift(np.fft.fft(res_sig)))\n",
    "max_dfp = max(discr_fourier_pts_res)\n",
    "Ad_max = [a * max_dfp for a in Ad] # нормализация Ad для вывода полос пропускания (изначально Ad принимает только значения 0/1)\n",
    "\n",
    "plt.figure(figsize=(45, 30))\n",
    "plt.title(\"Амплитудный спектр сигнала, пропущенного через фильтр\")\n",
    "plt.xticks(x_s)\n",
    "plt.plot(fk_8_khz, discr_fourier_pts_res)\n",
    "plt.plot(fk_8_khz, Ad_max)\n",
    "plt.legend([\"Амплитудный спектр\", \"Полосы пропускания\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "executionInfo": {
     "elapsed": 5845,
     "status": "ok",
     "timestamp": 1607949425521,
     "user": {
      "displayName": "19parenchenkov98",
      "photoUrl": "",
      "userId": "07404199301160036105"
     },
     "user_tz": -180
    },
    "id": "Gio1dtV6fyBz",
    "outputId": "0a003016-5518-4b56-fb24-d77ccfd2f88a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc5343f6400>]"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import signal\n",
    "w, H = signal.freqz(h_full,1,np.linspace(0,2000,1000),fs=8000)\n",
    "plt.plot(w,abs(H))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "snm_IYhuIzKP"
   },
   "outputs": [],
   "source": [
    "# расчёт кожффициентов фильтра с помощью готовой функции\n",
    "des = list([1 if (fk >= 50 and fk <= 150) or (fk >= 350 and fk <= 750) or (fk >= 900 and fk <= 1500) else 0 for fk in fk_8_khz[4000:]])\n",
    "h_fun = signal.firls(N * 2 + 1, np.linspace(0, 4000, 4000), des, fs=8000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-_WGHuHBJgB_"
   },
   "outputs": [],
   "source": [
    "res_sig_fun = np.convolve(x_1, h_fun, mode=\"same\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 847
    },
    "executionInfo": {
     "elapsed": 2836,
     "status": "ok",
     "timestamp": 1607949473124,
     "user": {
      "displayName": "19parenchenkov98",
      "photoUrl": "",
      "userId": "07404199301160036105"
     },
     "user_tz": -180
    },
    "id": "KEIbc73UJkRM",
    "outputId": "e0e48010-3c4a-4358-a9f5-09919ecf0f1a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc53429ef28>"
      ]
     },
     "execution_count": 52,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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36vHo+fm4vNCIhXozKqVS1KqlODZbi7sOTsXJg5Mxsf/d77my0IinLi/GlYVGXFlsRDPNYqJcirnJStx7dDpedHSmF8o6d205XnWq3a9quRQ/9db743/5wZfE+ev1eOrSQswvN2Oh0YyFeisWG81YbLRiodGM60vNmHq6FLEY8U8+9UTM/9FzUS4l8drTh+JtZ07Ef3nmRLzsjrlIkr0bvxisLMviwo16fOu56/H1Z67FJx4+Hw89cy1ma5X4xfe9Mn7s++7Z0vEslZL46bfdH+96xcn4x598In7jS2fjX33+qZisluKVdx+M+47NxKnD03HXoak4OjsRR6Yn4vD0RByeqcZsrTJU50x3quPnry3l3BIAAABgGAxFGDBJknJE/JOIeEdEPBMRX0qS5D9mWbb513sBAGCItdJsy0EPRl+WZbHSymK52Yr6Shr1ZiuW1/0sX7ocb4yIPz17Na6WX4iJSqn9r9wOb0xVyzFZLcdkpRyTE+37h+mDaEZPlmVxbWklnr68GM9cWYpnryzFs1eX4pkrS/E95x+LvxER7/yHn4nHG0cizdqVyqYnynFoeiJOHZ6KU4en4r5jM/HgnQfje+6ci6O7mILxz85e7f2+2GjG9MTO/ztjderV9vSri/VWLK10/jWasdRoxdJK2v69d38aSyvdx1qxUG/F5YV28K/eTDfczsGpapyYq8Ub7jsSrzp1KH7wpcfi/hNzO2rzi4/Pxm/9z2+Kzz1xKf7tF5+Oj33jXPzml5+JiIhyKYnjs7W440AtThyYjDsO1OLI9EQc6oRWDk2vhlgOzVRjbshCLOOglWbx8PPX41OPvhCffOSF+NrZq5FmEYenq/Hm+4/FW+4/Fm968dE4+i8m4l3fczLe9edfs+1t3H9iNiIiHjk3Hxfm6/Ev/+TJeOTc/C2fkyQRfzB1I5auX40//MRjcebkXLzs5FycPjId1QFVRsuyLC7M1+PxF27E4+fn4yVPXIzXr7TiBz70ibh4o3HL585NVuKHX3Fn/I//xX1x9spi/MhrT61rfxInD07GyYO3qYT4J/dG/OeIz/6tt8efnm/Gl568HJ969EL80scfjV/6+KNx96GpeNuZ4/GW+4/FG+47Gkdm8oxIjqdmK42rSytxdXElri424sriSlxZaMSFG/U4f305Xrhejxfml+P89XpcmK9Ho7U67n7vqYPx8+8+Ez/2fafj4HR129t+yfHZ+JW/8ur4P/6rV8SnH70QX3nqSjz0zNX4o0cvxIX5+obPqZaT3th6aLoac5M3h3bnJqsxUyvHVLUc0xOVmJooxVS1ElMT5ZieaL+mm54oD+Ra615LX3/2WjRb6Y4rGzZbaXz34kI8fG4+Hj13PR47fyPOX1+OC/P1uLTQiDTNIs2yKCVJPDKRxb/7wlPxkcf+JO4+PBX3HJ6Oe45MxT1HpuOew9Nx58HJwlVYpJhaabYaAq+3fzZaaTSaaay00pi4cCneGBFfffpqXE3OR6VUimq5FNVyEhOVUkxWy1GrlKJWKcdktf2zVint6ZdcKIZWmkUpCa+bAQAopKEIA0bEGyLiiSzLvhMRkSTJv4uIvxgRwoAjKkvTuHLx3N6tf8/WHJHt4cqzPW353hlkqwe1fwe6LwfWpsHJBngi7uU5Pajtbud4brc/21l8e23eZju29YTtrX2g7b7FArc7Trdqx+22u5t1387tnnurbW/03M2W3ui63WjZjduztQ1tvu3BtmfTfbLFNm11nZsdm43v3lp/Nm7jJmvccL9trT3feu56fPiPvxP/3ZteFPcdn4lKkkS5lES5VIr+z8T619e/nrX3Z70Fst7j2U3LZpGt/t6/bOe3rG8ja5bta0Bv2az//tXnZ1mnAljfMt22ZN3nZ33L9u3d9nPXPdbdzrr+9D9283r7HovVx7r7ZaP1Z1kWadqeQq37/DTLIs0i0r7H087jad/j7dtZpGkWrSyLZppFK+3cTrNodta7ejvtPd7M2ve10ixWWultx5uXJM/Fb9ciPvyZ78RHP/WlWy8c7RDHZKUcUxPlmOx8kNX+V4pyKYkkOh9aJBFJZ/kkkuh+lnHz7d4zOo+t3rf6+UfS99jG64u+53Y2f9P6Vtt18/p660rWbm99m/u3sfFj6/q1vr0bPCduavMG20+SDbdXLiVR6lzrpVIS5SSJUhK938ulZM3zb6WURJSTJJLu+jrrKSWr66qUOz9L3Z/t414tr72dRfv87p6b3XO42Wr/bLTSuLrYiEsLjXa4bb4ez1xZiqcvL8bZy4sxX2+uadtsrRJ3H5qKN09VIxYj3vfgTKzMHI1SqRSNTnDuyuJKPHftUnzxuaX4+MJq2Of4XC0eODEXLzk+Ey8+PhsvPj4TdxyYjJVmGssraSw1W1FfaUUpSaJaKUUSEd++cCO++tSV+Pg3zsXhaIc/fvdzD8Xxucl4/tpSXF9qRpJElEulODxdiWNztTg2W4u5yWpcWWjE5Wcfiz8XEf/355+K3/7s5+LpSwtxZXHltsega6q6GtroD3AcnZ2Il94xF8dmJ+Lo7EQcm63F0dlaHJ2ZiONztTg8PTHwan1JksRbHjgWb3ngWDRbaTx2/kY8/sJ8PPHCjTh3bTnOz9fj7OXF+PKTl+Pq0sqmY06llMSh6WpMTZTbYeNKOWrVUtQqpd553L1uSkl0bre3X+qc96VS9C2T9D4oTbrLd+8vRbSvkCwazfYYudJK1/y+0sxipf/3Vtq+3fm90Wo/9lfjm/EL5Ygf+eX/FEuVQ1GtlKJWTqJWLcdUpRyTE93AdCkmJ0pRK7ePWa1aimqp1LkWo3eNttuXRDmSNfeVS+1l2vumFBOddU5UVvdRM82i2UrbfwNaWTSzNJqtzt+AVhb1ZhrPXVuKs5eX4tFz8/H1Z6/2pvx98M4D8bM/cDx+4IFj8fI7D0SpNyYsR2QbB0u34uBUNR44MRt/7w8ejYiIV959MD7w7jPxirsOxvG5WsxOVqLZal9rL8wvx/NXl+OZK4sx+eVSXKsvxb/+5Fd650yllMTdh6fj9JHpuOfIdJyYnYjDM7U4MlON2clKL0hR6eyLblB2sdGKizfqveDW2StL8Z0LN2J+eXUc+cDk5Ygk4u1n7ogH7piNFx2diaOz7bBqpZzESiuLq4uNePLSQnzuiUvxka89G7/x5bMREfHq04d2vH8iIg6m1+Ktpw7EW08dj7/5luPxwo3l+OzjF+Ozj1+MT3714fj9/699jB44MRtnTh6IFx+fiXuOTMfh6Yk4OFWNmcly7/xOoj2+dsfTRiuLVppGs/M3v9lqnyMraRr1ZvucX262otFMo77SDqq00ohmmkXWG5vTaGURaWc9adZed+81SBbRbKax3ExjeaUZy52gcr2Zxm/WG/E7n38yPvS5j/X+TpQ653ulXIpqqT2utgMw7RDM2p8b/54kEa20/Xqolfa/RureXvsaq9nZH5v9zVnp3t/Keo/Xm60158h6ByYrvaDzG+47EicO1OLkgck4c/JAPHjXgTg4tf0A4EZma5V4z6vujPe86s7efcsrrXj+2nJcXmj0Klhe6QsrXllsxJWFlXjmymLMLzdjfnklbtSb25qut1pOYrJajgOT1Xj73FPxdyLi//3aY5FcvyvuPDgVk9VyXJhfjnPXl+PSjXrUm+1za6bWDt8fnq7Gnz367TgcS7Fw5Xr8xD/+WLzjwTvi5XcdjLsOTUaj1Q72LzdaUS0nUel80aRSSuLc9aV44vxCfPvCjXj8hRvx5MWFXtCyUkri9JGZuOfgZLz66EQcmZ6OSrk9TjbTLJKvRBybrUW1XIqvPn0l/tNDz0err+PlUhInD0x2vhwwHacOT8UdBybj4FQ1Dk1X4+BUNaYnyu3XK+X265lSkvTOre751TvPsrjt/a3OtdJqZauvzzvLRnRf6699H3PT+4v+9xHZ+vcyG78PivXP2+C+6Fu+f1sRNz8vNnhP015u8/dG3cejb73ddd90X/d9TnTfF3WWzPr20br29W9g/XvS7rKrz+17b9W/vb73Wf3Ho3sco2986S6fdZZbfa/WHm+WV1q98N9mX8roejB5Mj5ai/g3n/56fDIt33LZfhPlzhezOuHAiUopJiulqFZKvdc93ddFkUSUovPeoNT3HqHv9VR3+d77m/7XVP3vYfqWX/N+o2/5iO5rr8593fcY3dux+r5l7TKx+rqj/3Vdd129NnTa2rf8an/XWv9+bM1j63dq/7KbP9R5POm/sel6b/m8dQvftM1193RvZRGxUF+JD//xd2O6Vo7/9c+9NBrN9t/2/mPRf4xj3b5cr/89aPv2zfui/331TW3csB+br6P/uclGz73NcmvavvHdneds/Oit3vJu+tD2Hxhsm2/5nM22s/3+b3cbt9zODta32bpuaZtP2e4Wtr+/tveE7a5/B3toV9sb5PZ3v+2dryDP3PSgt70XIfCBr3HQfR7wCvfifMjxFNvUbvfbIPbTbldx4PCxqFR9IXJUJYMMlOy4EUnyoxHxrizL3t+5/eMR8cYsy35ms+e8/vWvz7785S/vVxMZsKWF+Zj6e6duvyAAAIyZp3/ow3Hp1DtipZVFo5lGo9WuJri00q4i2P7ZDk2tv2+5c7v7oefa0OVGHyiu/VCz/cAmH0BuFgBd91h3WxtvY+36Yt3j69uc9a3s5g9u134IGevbsG6ba9uVbbg+2tX9Th2eitNHVoNA9xxpf4h/6tB0HJjqVJV76Lcifuf9eTd3y36h+tfjiTveGaePzMSpw1NxcKpduenAZDVmapWYqpbbVZsmKr0AYK1S3OqbrTSL60srvdDK1cV22PPq4up99ZVW1JvtyqT1Zhr1lbQX6NnoQ/jVD/RvDgykfff3P9b9QD9J2h+qdwNGlXIpJsqdkFLnvolyKSp9v1fLpahWVsNJr37hI/HDT/7dnPfsPnjD/xTxw7+0o6f+yicei1/9w8fjZ952f/yNH3rp1s7fD7894tl9/v+1yUMRH3hqS4t+9+J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\n",
      "text/plain": [
       "<Figure size 3240x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discr_fourier_pts_res_fun = abs(np.fft.fftshift(np.fft.fft(res_sig_fun)))\n",
    "max_dfp_fun = max(discr_fourier_pts_res_fun)\n",
    "Ad_max = [a * max_dfp_fun for a in Ad] # нормализация Ad для вывода полос пропускания (изначально Ad принимает только значения 0/1)\n",
    "\n",
    "plt.figure(figsize=(45, 30))\n",
    "plt.title(\"Амплитудный спектр сигнала, пропущенного через фильтр (Scipy)\")\n",
    "plt.xticks(x_s)\n",
    "plt.plot(fk_8_khz, discr_fourier_pts_res_fun)\n",
    "plt.plot(fk_8_khz, Ad_max)\n",
    "plt.legend([\"Амплитудный спектр\", \"Полосы пропускания\"])"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "lab3_1.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
